App-based crowd sourcing of bicycle and pedestrian conflict data : final report.

Details

• Creators:
  Mattingly, Stephen P. ; Casey, Colleen ; Johnson, Taylor
• Corporate Creators:
  University of Texas at Arlington
• Corporate Contributors:
  Western Michigan University. Transportation Research Center for Livable Communities
• Subject/TRT Terms:
  Active Transportation Apps Crowdsourcing Cyclists Data Analysis Mobile Applications Conflict Analysis Pedestrians Traffic Conflicts Transportation Safety
• Publication/ Report Number:
  TRCLC 15-7
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Western Michigan University. Transportation Research Center for Livable Communities
• Abstract:
  Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data
  
  to assess transportation safety; however, in the context of public health where perceptions of safety may influence
  
  the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other
  
  similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear
  
  conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g.
  
  change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver
  
  occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle
  
  conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this has
  
  focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better
  
  understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by
  
  pedestrians and cyclists; the study also tests the effectiveness of the app and its associated crowd-sourced data
  
  collection. This study assesses the data quality of the crowd sourced data and compares it to more traditional data
  
  sources while performing hot spot analysis. If widely adopted, the app will enable communities to create their own
  
  data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data
  
  source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement,
  
  infrastructure, programs and policies.
  
  
• Format:
  PDF
• Funding:
  TRCLC 14-01
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:71c685c05fcd94a11f70facd76298b860a143dea899b1a06d70ce36c6006873c2c022ae7f88a2d9c87c2ad0947460da6c91b4767336248045bb0d73f6faca71e
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32318/dot_32318_DS1.pdf
• File Type:
  [PDF - 7.81 MB ]